Precision medicine represents an evolving approach to improve treatment efficacy by modifying it to individual patient's gene variation. Pharmacogenetics, an applicable branch of precision medicine, identifies patient's predisposing genotypes that alter the clinical outcome of the drug, hence preventing serious adverse drug reactions. Pharmacogenetics has been extensively applied to various fields of medicine, but in the field of anesthesiology and preoperative medicine, it has been unexploited. Although the US Food and Drug Administration (FDA) has a table of pharmacogenomics biomarkers and pharmacogenetics, this table only includes general side effects of the included drugs. Thus, the existing FDA table offers limited information on genetic variations that may increase drug side effects. Aims: The purpose of this paper is to provide a web-based pharmacogenomics search tool composed of a comprehensive list of medications that have pharmacogenetic relevance to perioperative medicine that might also have application in other fields of medicine. For this investigation, the FDA table of pharmacogenomics biomarkers in drug labeling was utilized as an in-depth of drugs to construct our pharmacogenetics drug table. We performed a literature search for drug-gene interactions using the unique list of drugs in the FDA table. Publications containing the drug-gene interactions were identified and reviewed. Additional drugs and extracted gene-interactions in the identified publications were added to the constructed drug table. Our tool provides a comprehensive pharmacogenetic drug table including 258 drugs with a total of 461 drug-gene interactions and their corresponding gene variations that might cause modifications in drug efficacy, pharmacokinetics, pharmacodynamics and adverse reactions. This tool is freely accessible online and can be applied as a web-based search instrument for drug-gene interactions in different fields of medicine, including perioperative medicine. In this research, we collected drug-gene interactions in a web-based searchable tool that could be used by physicians to expand their field knowledge in pharmacogenetics and facilitate their clinical decision making. This precision medicine tool could further serve in establishing a comprehensive perioperative pharmacogenomics database that also includes different fields of medicine that could influence the outcome of perioperative medicine.
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http://dx.doi.org/10.3390/jpm10030065 | DOI Listing |
Schizophrenia (Heidelb)
January 2025
Suzhou Guangji Hospital, Suzhou, Jiangsu Province; Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu Province, China.
Numerous observational studies have highlighted associations between mitochondrial dysfunction and schizophrenia (SCZ), yet the causal relationship remains elusive. This study aims to elucidate the causal link between mitochondria-associated proteins and SCZ. We used summary data from a genome-wide association study (GWAS) of 66 mitochondria-associated proteins in 3,301 individuals from Europe, as well as a GWAS on the large, multi-ethnic ancestry of SCZ, involving 76,755 cases and 243,649 controls.
View Article and Find Full Text PDFBioinformatics
January 2025
College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
Motivation: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph motifs of (disease, drug, gene) triplets. Among them, the triangle is a steady and important motif structure in the network, and other various motifs different from the triangle also indicate rich semantic relationships.
View Article and Find Full Text PDFJ Clin Exp Dent
December 2024
DDS. Titular Professor. Universidad de Antioquia U de A, Medellín, Colombia. Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Medellín, Colombia.
Background: The RTK-VEGF4 receptor family, which includes VEGFR-1, VEGFR-2, and VEGFR-3, plays a crucial role in tissue regeneration by promoting angiogenesis, the formation of new blood vessels, and recruiting stem cells and immune cells. Machine learning, particularly graph neural networks (GNNs), has shown high accuracy in predicting these interactions. This study aims to predict drug-gene interactions of the RTK-VEGF4 receptor family in periodontal regeneration using graph neural networks.
View Article and Find Full Text PDFNan Fang Yi Ke Da Xue Xue Bao
January 2025
Provincial School of Clinical Medicine, Fujian Medical University; Department of Respiratory and Critical Care Medicine, Fujian Provincial Hospital of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, China.
Objectives: To identify the key genes and immunological pathways shared by type 2 diabetes mellitus (T2DM) and chronic obstructive pulmonary disease (COPD) and explore the potential therapeutic targets of T2DM complicated by COPD.
Methods: GEO database was used for analyzing the gene expression profiles in T2DM and COPD to identify the common differentially expressed genes (DEGs) in the two diseases. A protein-protein interaction network was constructed to identify the candidate hub genes, which were validated in datasets and disease sets to obtain the target genes.
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